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Asymptotically Unbiased Estimation of the Coefficient of Tail Dependence
Author(s) -
GOEGEBEUR YURI,
GUILLOU ARMELLE
Publication year - 2013
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2012.00800.x
Subject(s) - mathematics , estimator , u statistic , bivariate analysis , asymptotic distribution , statistics , efficiency , efficient estimator , delta method , extremum estimator , minimum variance unbiased estimator , m estimator , function (biology) , tail dependence , multivariate statistics , evolutionary biology , biology
. We introduce and study a class of weighted functional estimators for the coefficient of tail dependence in bivariate extreme value statistics. Asymptotic normality of these estimators is established under a second‐order condition on the joint tail behaviour, some conditions on the weight function and for appropriately chosen sequences of intermediate order statistics. Asymptotically unbiased estimators are constructed by judiciously chosen linear combinations of weighted functional estimators, and variance optimality within this class of asymptotically unbiased estimators is discussed. The finite sample performance of some specific examples from our class of estimators and some alternatives from the recent literature are evaluated with a small simulation experiment.